Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A speech to text conversion system comprising: a natural language processing computing device in communication with a phonetic name database, the natural language processing computing device configured to: receive, from a user computing device, at least one proper name uttered by a user; apply a phonetic code algorithm to the at least one proper name uttered by the user to create a phonetic code; compare the created phonetic code to a plurality of predetermined phonetic proper name codes stored in the phonetic name database, the comparison performed using at least one phonetic matching algorithm; in response to none of the plurality of predetermined phonetic proper name codes in the phonetic name database matching the created phonetic code in the comparison, i) provide another voice prompt to the user to speak a full spelling of the at least one proper name, and ii) add, in the phonetic name database, the full spelling of the at least one proper name received from the user in association with the plurality of predetermined phonetic proper name codes; in response to the created phonetic code matching one of the plurality of predetermined phonetic proper name codes in the phonetic name database, determine from the phonetic name database whether multiple different spellings of the matching predetermined phonetic proper name code exist; when multiple different spellings of the matching predetermined phonetic proper name code are determined to exist, either: apply, using the at least one phonetic matching algorithm, a first phonetic code string from the phonetic name database to recognize an utterance of the user providing a corresponding natural language cue regarding which of the multiple different spellings of the at least one proper name is correct, wherein the natural language cue includes a separate enunciation of i) the matching predetermined phonetic proper name code and ii) a letter that is unique to one of the multiple different spellings, wherein the first phonetic code string does not spell an entirety of the one of the multiple different spellings; or apply, using the at least one phonetic matching algorithm, the first phonetic code string to provide a voice prompt to the user including the corresponding natural language cue regarding which of the multiple different spellings of the at least one proper name is correct; and convert the matching predetermined phonetic proper name code to text including the correct one of the multiple different spellings of the at least one proper name.
This invention relates to a speech-to-text conversion system designed to accurately transcribe proper names, addressing the challenge of phonetic ambiguity in spoken language. The system includes a natural language processing computing device connected to a phonetic name database. When a user speaks a proper name, the system generates a phonetic code for the utterance and compares it against stored phonetic codes in the database. If no match is found, the system prompts the user to spell the name aloud and adds the spelling to the database. If a match is found but multiple spellings exist for the phonetic code, the system uses phonetic matching algorithms to recognize natural language cues from the user, such as a unique letter or separate enunciation, to determine the correct spelling. Alternatively, the system may provide a voice prompt to guide the user in clarifying the spelling. The system then converts the recognized phonetic code into text with the correct spelling. This approach improves accuracy in transcribing names with multiple possible spellings, enhancing the reliability of speech-to-text systems in applications like voice assistants, transcription services, and automated customer support.
2. The system of claim 1 , wherein the natural language processing computing device is further configured to: apply the first phonetic code string wherein the natural language cue further includes an enunciation of a connecting phrase between i) the matching predetermined phonetic proper name code and ii) the letter that is unique to the one of the multiple different spellings, wherein the connecting phrase is one of “with a”, “with an”, “without a”, “without an”, and “with no”.
This invention relates to a system for processing natural language input to disambiguate phonetic spellings of proper names. The system addresses the challenge of accurately interpreting spoken or written proper names that have multiple valid spellings, such as "Katherine" versus "Catherine," by leveraging phonetic codes and contextual cues. The system includes a natural language processing (NLP) computing device that generates a phonetic code string from a spoken or written input. The NLP device compares this string to a database of predetermined phonetic proper name codes to identify potential matches. When multiple spellings exist for a phonetic match, the system further analyzes the input for a natural language cue—a connecting phrase like "with a," "with an," "without a," "without an," or "with no"—that precedes a unique letter distinguishing the spellings. For example, if the input includes "Katherine with a K," the system uses the connecting phrase to select the correct spelling. This approach improves accuracy in name recognition by incorporating contextual linguistic patterns. The system may also apply the phonetic code string to other parts of the input, such as non-name elements, to enhance overall processing. The invention is particularly useful in applications like voice assistants, transcription services, and search engines where precise name interpretation is critical.
3. The system of claim 1 , wherein the natural language processing computing device is further configured to: apply the first phonetic code string wherein the natural language cue further includes the letter that is unique being a consonant.
This invention relates to a natural language processing system designed to enhance speech recognition accuracy by leveraging phonetic and linguistic cues. The system addresses the challenge of accurately interpreting spoken language, particularly when dealing with homophones or similar-sounding words, by incorporating phonetic encoding and unique letter analysis. The system includes a natural language processing computing device that processes input speech data to generate a phonetic code string. This phonetic code string is derived from the input speech and is used to disambiguate words that sound alike but have different meanings. The system further analyzes the input for natural language cues, such as unique consonants, to refine the interpretation of the spoken words. Specifically, the system applies the phonetic code string while considering the presence of a unique consonant in the input, which helps distinguish between words that might otherwise be misinterpreted. The phonetic code string is generated by converting the input speech into a sequence of phonetic symbols, which are then mapped to a standardized code. This code is used to compare and match the input speech against a stored lexicon of words and their corresponding phonetic representations. The system then evaluates the input for natural language cues, such as the presence of a unique consonant, to further refine the matching process. By combining phonetic encoding with linguistic analysis, the system improves the accuracy of speech recognition, particularly in contexts where homophones or similar-sounding words are likely to cause confusion.
4. The system of claim 1 , wherein the natural language processing computing device is further configured to: provide the voice prompt to the user including the letter that is unique being a vowel.
The invention relates to a system for assisting users in learning or recognizing letters, particularly focusing on vowels. The system includes a natural language processing computing device that generates voice prompts to guide the user. These prompts include a specific letter that is unique, such as a vowel, to help the user identify and differentiate it from other letters. The system may also include a display device to visually present the letter or related content, ensuring the user can both hear and see the letter for better comprehension. The voice prompt is designed to highlight the uniqueness of the vowel, making it easier for the user to recognize and remember it. This approach is useful in educational applications, particularly for children or individuals learning a new language, where distinguishing vowels from consonants is a fundamental step in literacy development. The system may further include input mechanisms, such as a microphone or touch interface, to allow the user to interact with the system, confirming their understanding or providing feedback. The overall goal is to enhance letter recognition through a combination of auditory and visual cues, ensuring effective learning outcomes.
5. The system of claim 1 , wherein the natural language processing computing device is further configured to: provide the voice prompt to the user including a plurality of successive natural language prompts that when answered by the user converge on the correct one of the multiple different spellings of the at least one proper name.
This invention relates to a natural language processing system designed to assist users in accurately selecting the correct spelling of proper names, such as personal or place names, from multiple possible variations. The system addresses the challenge of resolving ambiguity in proper name spellings, which can arise due to variations in pronunciation, regional dialects, or user input errors. The system includes a computing device that generates voice prompts to guide the user through a series of questions. These prompts are structured as successive natural language queries that progressively narrow down the possible spellings of the proper name. Each response from the user refines the system's understanding, leading to the correct spelling. For example, the system may first ask for the general category of the name (e.g., "Is this a person's name or a place name?") and then follow up with more specific questions (e.g., "Does the name contain an 's' or a 'z'?"). The prompts are designed to be intuitive and conversational, ensuring the user can easily provide the necessary information without prior technical knowledge. The system dynamically adapts the prompts based on the user's responses, ensuring efficiency and accuracy. This approach minimizes user frustration and reduces the likelihood of errors in name spelling, which is particularly useful in applications such as voice assistants, transcription services, or database entry systems where accurate name representation is critical. The invention improves upon existing methods by providing a structured, interactive way to resolve spelling ambiguities without requiring manual intervention or extensive user input.
6. The system of claim 1 , wherein the natural language processing computing device is further configured to: if a number of the multiple different spellings exceeds a predetermined limit, provide the voice prompt to the user instructing the user to speak a full spelling of the at least one proper name.
This invention relates to a system for improving the accuracy of natural language processing (NLP) when handling proper names in voice input. The problem addressed is the difficulty in accurately recognizing and transcribing proper names due to variations in pronunciation, spelling, and regional dialects, which can lead to errors in voice-based applications such as virtual assistants, transcription services, or search systems. The system includes a natural language processing computing device that processes voice input from a user. When the system detects multiple different spellings of a proper name in the input, it evaluates whether the number of variations exceeds a predetermined threshold. If the threshold is exceeded, the system generates a voice prompt instructing the user to speak the full spelling of the proper name. This ensures that the system can accurately capture and transcribe the name, reducing errors in subsequent processing tasks such as search, data entry, or command execution. The system may also include additional components, such as a voice input device for capturing the user's speech and a display for providing feedback or instructions. The NLP device may use machine learning models to analyze speech patterns and identify potential name variations, applying rules or statistical methods to determine when a prompt is necessary. The predetermined limit for spellings can be adjusted based on context, user preferences, or system performance metrics to balance accuracy and user convenience. This approach enhances the reliability of voice-based systems in applications where proper name recognition is critical.
7. The system of claim 1 , wherein natural language processing computing device is further configured to: create the phonetic code using the at least one phonetic matching algorithm, the at least one phonetic matching algorithm including at least one of a Soundex phonetic algorithm, a Metaphone phonetic algorithm, and a Beider-Morse phonetic matching algorithm.
This invention relates to a system for processing and matching phonetic representations of text using natural language processing (NLP) techniques. The system addresses the challenge of accurately identifying and matching words or names that sound similar but may be spelled differently, which is common in applications like search engines, databases, or identity verification systems. The system includes a natural language processing computing device that generates phonetic codes from input text. These phonetic codes are numerical or alphanumeric representations derived from the pronunciation of words, enabling efficient comparison of phonetically similar terms. The system employs at least one phonetic matching algorithm to create these codes, with options including the Soundex algorithm, the Metaphone algorithm, and the Beider-Morse algorithm. Each algorithm has distinct rules for encoding phonetic sounds, allowing flexibility in handling different languages or dialects. The Soundex algorithm, for example, converts names into codes based on consonant sounds, while Metaphone and Beider-Morse offer more refined approaches, accounting for variations in pronunciation across languages. The system may use one or a combination of these algorithms to improve accuracy in matching phonetically similar terms, reducing errors in applications like spell-checking, data deduplication, or record linkage. The phonetic codes generated can then be compared to identify potential matches, even when input text contains spelling variations or transcription errors.
8. The system of claim 1 , wherein the natural language processing computing device is further configured to: obtain at least one location-based parameter of a user; and select one of a plurality of name groupings in the phonetic name database based upon the obtained at least one location-based parameter.
The system relates to natural language processing (NLP) for phonetic name matching, addressing challenges in accurately identifying and grouping names with similar pronunciations across different languages and regions. The system includes a phonetic name database storing names categorized into multiple groupings, each representing distinct phonetic variations. A natural language processing computing device processes user input to match names against the database, improving accuracy in applications like voice recognition, search, or communication systems. The device is further configured to obtain location-based parameters of a user, such as geographic coordinates or regional settings, and dynamically select the most relevant name grouping from the database based on these parameters. This ensures that phonetic matches align with regional pronunciation conventions, enhancing precision in name recognition tasks. For example, a user in a Spanish-speaking region may receive phonetic matches optimized for Spanish pronunciation patterns, while a user in an English-speaking region would receive matches optimized for English. The system adapts to linguistic and regional variations, reducing errors in name-based interactions.
9. The system of claim 1 , wherein the natural language processing computing device is further configured to: provide a confirming voice response to the user including a natural voice acknowledgment of a proper spelling of the at least one proper name.
A system for natural language processing (NLP) enhances user interaction by accurately recognizing and confirming proper names in spoken input. The system includes a computing device with NLP capabilities that processes audio input from a user to identify spoken words, including proper names. When a proper name is detected, the system verifies its spelling and pronunciation. The system then generates a confirming voice response, using a natural-sounding voice, to acknowledge the correct spelling of the identified proper name. This feature improves user experience by reducing errors in name recognition and providing immediate feedback. The system may also include additional components such as speech recognition modules, databases for name validation, and voice synthesis tools to ensure accurate and natural-sounding responses. The technology addresses challenges in voice-based interfaces where proper names are often misinterpreted, ensuring reliable and user-friendly interactions.
10. The system of claim 1 , wherein the natural language processing computing device is further configured to: send the converted text to an external computer system as a digital input.
The system involves natural language processing (NLP) for converting spoken or written input into a structured digital format. The core technology addresses the challenge of integrating unstructured human language into automated systems, enabling seamless interaction between users and digital platforms. The NLP computing device processes input data, such as speech or text, and converts it into a standardized digital format. This conversion allows the system to interpret and act on user commands or queries, improving accessibility and efficiency in human-computer interactions. Additionally, the system includes a feature where the converted text is transmitted to an external computer system as a digital input. This ensures compatibility with other systems, allowing the processed data to be used in applications like customer service automation, voice-controlled devices, or data entry systems. The external transmission capability enhances interoperability, enabling the NLP system to function as part of a larger, interconnected digital infrastructure. The overall solution improves accuracy and reduces manual data handling, making it valuable for industries requiring efficient language processing and system integration.
11. The system of claim 1 , wherein the natural language processing computing device is further configured to: send the converted text to a multi-party payment network system for processing a payment card transaction.
A system for processing payment card transactions using natural language processing (NLP) is disclosed. The system addresses the challenge of integrating voice or text-based payment instructions into existing financial transaction networks. The NLP computing device converts spoken or written payment instructions into a standardized format compatible with multi-party payment networks, such as those used for credit or debit card transactions. This conversion ensures that payment details, including card numbers, amounts, and recipient information, are accurately extracted and formatted for secure transmission. The system then sends the converted text to the payment network, which processes the transaction as it would with traditional card-based inputs. This approach enables seamless integration of voice assistants, chatbots, or other NLP interfaces into payment workflows, improving accessibility and convenience for users. The system may also include additional components, such as authentication modules to verify user identity before processing transactions, ensuring security. By bridging NLP interfaces with established payment networks, the system facilitates faster, more intuitive payment processing while maintaining compatibility with existing financial infrastructure.
12. A method for electronically converting speech to text, the method implemented with at least one host computing device having at least one processor in communication with a phonetic name database, the method comprising: receiving, by the at least one host computing device from a user computing device, at least one proper name uttered by a user; applying, by the at least one host computing device, a phonetic code algorithm to the at least one proper name uttered by the user to create a phonetic code; comparing, by the at least one host computing device, the created phonetic code to a plurality of predetermined phonetic proper name codes stored in the phonetic name database, the comparison performed using at least one phonetic matching algorithm; in response to none of the plurality of predetermined phonetic proper name codes in the phonetic name database matching the created phonetic code in the comparison, i) providing another voice prompt to the user to speak a full spelling of the at least one proper name, and ii) adding, in the phonetic name database, the full spelling of the at least one proper name received from the user in association with the plurality of predetermined phonetic proper name codes; in response to the created phonetic code matching one of the plurality of predetermined phonetic proper name codes in the phonetic name database, determining, by the at least one host computing device from the phonetic name database, whether multiple different spellings of the matching predetermined phonetic proper name code exists; when multiple different spellings of the matching predetermined phonetic proper name code are determined to exist, either: applying, by the at least one host computing device using the at least one phonetic matching algorithm, a first phonetic code string from the phonetic name database to recognize an utterance of the user providing a corresponding natural language cue regarding which of the multiple different spellings of the at least one proper name is correct, wherein the natural language cue includes a separate enunciation of i) the matching predetermined phonetic proper name code and ii) a letter that is unique to one of the multiple different spellings, wherein the first phonetic code string does not spell an entirety of the one of the multiple different spellings; or applying, by the at least one host computing device using the at least one phonetic matching algorithm, the first phonetic code string to provide a voice prompt to the user including the corresponding natural language cue regarding which of the multiple different spellings of the at least one proper name is correct; and converting, by the at least one host computing device, the matching predetermined phonetic proper name code to text including the correct one of the multiple different spellings of the at least one proper name.
This invention relates to speech-to-text conversion, specifically addressing the challenge of accurately transcribing proper names, which often have multiple valid spellings or phonetic variations. The system uses a host computing device with a processor and a phonetic name database to process spoken proper names. When a user speaks a name, the system generates a phonetic code and compares it to stored phonetic codes in the database. If no match is found, the system prompts the user to spell the name aloud and adds the spelling to the database. If a match is found but multiple spellings exist, the system uses natural language cues—such as enunciating a unique letter or part of the name—to determine the correct spelling. The system then converts the phonetic code to text using the verified spelling. This approach improves accuracy in transcribing names with phonetic ambiguities, reducing errors in speech-to-text applications. The method dynamically updates the database with new names and their spellings, enhancing future recognition accuracy.
13. The method of claim 12 , further comprising: applying, by the at least one host computing device, the first phonetic code string wherein the natural language cue further includes an enunciation of a connecting phrase between i) the matching predetermined phonetic proper name code and ii) the letter that is unique to the one of the multiple different spellings, wherein the connecting phrase is one of “with a”, “with an”, “without a”, “without an”, and “with no”.
This invention relates to a method for improving the accuracy of speech recognition systems, particularly when processing natural language cues that include proper names with multiple possible spellings. The problem addressed is the ambiguity that arises when a spoken proper name can be spelled in different ways, leading to errors in transcription or data entry. The method involves generating a phonetic code string for a spoken input that includes a proper name, where the phonetic code string is compared against a database of predetermined phonetic proper name codes to identify a matching code. The method further includes analyzing the spoken input for a natural language cue that specifies a unique letter distinguishing one spelling from another. For example, if the name "Katie" can be spelled with or without a "y," the natural language cue might include a phrase like "Katie with a y." The method then applies the phonetic code string to resolve the ambiguity by incorporating the specified letter into the final transcription. Additionally, the method may include a connecting phrase between the proper name and the unique letter, such as "with a," "with an," "without a," "without an," or "with no," to clarify the spelling variation. This approach enhances the accuracy of speech recognition by leveraging contextual cues to disambiguate proper names with multiple spellings.
14. The method of claim 12 , further comprising: applying, by the at least one host computing device, the first phonetic code string wherein the natural language cue further includes the letter that is unique being one of a consonant and a vowel.
The invention relates to a method for processing natural language cues in a computing system to improve speech recognition or text analysis. The method involves analyzing a natural language cue, which is a segment of text or speech, to identify a phonetic code string. This phonetic code string is derived from the natural language cue and represents its phonetic characteristics. The method further includes applying this phonetic code string to enhance processing, such as improving speech recognition accuracy or text analysis efficiency. A key aspect of the method is that the natural language cue includes a letter that is unique, meaning it is either a consonant or a vowel that does not appear elsewhere in the cue. This uniqueness helps in distinguishing the cue from other similar inputs, ensuring more accurate processing. The method is executed by at least one host computing device, which may include servers, cloud-based systems, or other computing platforms capable of handling natural language processing tasks. The phonetic code string is applied to refine the interpretation of the natural language cue, leveraging the unique letter to improve the system's ability to recognize or analyze the input correctly. This approach is particularly useful in applications where precise phonetic or linguistic distinctions are critical, such as voice assistants, transcription services, or language learning tools.
15. The method of claim 12 further comprising: if a number of the multiple different spellings exceeds a predetermined limit, providing the voice prompt to the user instructing the user to speak a full spelling of the at least one proper name.
This invention relates to voice recognition systems that handle proper names, particularly when multiple different spellings are detected for the same name. The problem addressed is the ambiguity that arises when a voice recognition system encounters variations in how a user pronounces or spells a proper name, leading to potential errors in transcription or data entry. The solution involves a method that monitors the number of different spellings generated for a proper name during voice input. If the number of detected spellings exceeds a predetermined threshold, the system provides a voice prompt instructing the user to speak the full spelling of the name. This ensures accuracy by directly capturing the intended spelling from the user when ambiguity is detected. The method may also include steps to compare detected spellings against a database of known names or to analyze pronunciation patterns to reduce errors before prompting the user. The system dynamically adjusts its behavior based on the detected variations, improving the reliability of name recognition in voice-based applications. This approach is particularly useful in applications like virtual assistants, transcription services, and data entry systems where accurate name recognition is critical.
16. The method of claim 12 , further comprising: creating the phonetic code using the at least one phonetic matching algorithm, the at least one phonetic matching algorithm including at least one of a Soundex phonetic algorithm, a Metaphone phonetic algorithm, and a Beider-Morse phonetic matching algorithm.
This invention relates to phonetic matching algorithms used in data processing systems to improve the accuracy of text-based searches, particularly for names or words that may be misspelled or phonetically similar. The problem addressed is the difficulty in accurately retrieving records or data entries when input queries contain variations in spelling or pronunciation, which is common in names, addresses, or other text fields. Traditional exact-match searches fail to account for these variations, leading to incomplete or incorrect results. The method involves generating a phonetic code for a given text string using at least one phonetic matching algorithm. The phonetic code is a standardized representation of how the text sounds, allowing for the comparison of phonetically similar strings. The method includes the use of multiple phonetic algorithms, such as Soundex, Metaphone, or Beider-Morse, to enhance accuracy. Soundex is a well-known algorithm that converts names into codes based on pronunciation, while Metaphone and Beider-Morse are more advanced algorithms that handle a broader range of languages and phonetic variations. By applying these algorithms, the system can match text strings that are phonetically similar but not identical in spelling, improving search results in databases, record-keeping systems, or other applications where text accuracy is critical. The method ensures that variations in spelling or pronunciation do not prevent the retrieval of relevant data.
17. The method of claim 12 , further comprising: obtaining at least one location-based parameter of a user; and selecting one of a plurality of name groupings in the phonetic name database based upon the obtained at least one location-based parameter.
This invention relates to a system for improving name recognition and retrieval by leveraging location-based data. The core problem addressed is the difficulty in accurately identifying and retrieving names from a phonetic name database, particularly when dealing with names that have multiple possible spellings or pronunciations. The solution involves enhancing a phonetic name database by incorporating location-based parameters to refine name selection. The method includes obtaining at least one location-based parameter of a user, such as geographic coordinates, regional dialect information, or local naming conventions. Using this data, the system selects the most relevant name grouping from the phonetic name database. This ensures that the retrieved names align with the linguistic and cultural context of the user's location, improving accuracy in name recognition and retrieval. The phonetic name database itself contains multiple name groupings, each associated with different phonetic representations or spellings of the same name. By filtering these groupings based on location, the system avoids ambiguity and provides more precise results. This approach is particularly useful in applications like voice recognition, search engines, or contact management systems, where accurate name identification is critical. The location-based filtering reduces errors caused by regional variations in pronunciation or spelling, enhancing user experience and system reliability.
18. The method of claim 12 , further comprising: providing a confirming voice response to the user including a natural voice acknowledgment of a proper spelling of the at least one proper name.
This invention relates to voice-based systems for processing and confirming proper names, such as those used in voice assistants or transcription services. The problem addressed is the difficulty in accurately capturing and verifying proper names, which often have unique spellings or pronunciations that standard speech recognition systems struggle with. The invention improves upon prior systems by providing a confirming voice response to the user, including a natural voice acknowledgment of the correct spelling of the at least one proper name. This ensures the user can verify the accuracy of the transcribed or recognized name in real time, reducing errors and improving user confidence. The system may involve capturing the user's spoken input, processing it to identify proper names, and then generating a synthesized or recorded voice response that repeats the name and confirms its spelling. This feedback loop helps correct misinterpretations before they propagate into downstream applications, such as contact lists, search queries, or document transcriptions. The method may also integrate with other features, such as spell-checking algorithms or user correction prompts, to further enhance accuracy. By providing immediate auditory confirmation, the system addresses the limitations of visual-only feedback, which can be less effective for users with visual impairments or those multitasking. The invention is particularly useful in applications where name accuracy is critical, such as medical records, legal documentation, or customer service interactions.
19. The method of claim 12 , further comprising: sending the converted text to an external computer system as a digital input.
This invention relates to text conversion and transmission systems, specifically addressing the challenge of converting text from one format to another and securely transmitting the converted text to external computer systems. The method involves converting text data from a source format into a target format, ensuring compatibility with different systems or applications. The conversion process may include formatting adjustments, encoding changes, or structural transformations to ensure the text is properly interpreted by the receiving system. After conversion, the text is transmitted as a digital input to an external computer system, enabling seamless integration and processing. The external system may be a server, database, or another computational platform requiring the converted text for further operations. This method ensures efficient data exchange between disparate systems, reducing errors and improving interoperability. The invention may also include additional steps such as validating the converted text, encrypting the transmission, or logging the transfer for security and auditing purposes. By automating the conversion and transmission process, the invention enhances productivity and reliability in data handling workflows.
20. The method of claim 12 , further comprising: sending the converted text to a multi-party payment network system for processing a payment card transaction.
This invention relates to systems and methods for processing payment card transactions using converted text. The technology addresses the challenge of securely and efficiently handling payment transactions where transaction details are initially provided in a non-standard or unstructured format, such as text or speech. The method involves converting the unstructured transaction data into a standardized format suitable for processing by a payment network. This conversion may include extracting relevant payment details, such as card numbers, expiration dates, and transaction amounts, from the input text. The converted text is then transmitted to a multi-party payment network system, which processes the transaction by validating the payment details, authorizing the transaction, and facilitating the transfer of funds between the involved parties. The system ensures secure transmission and processing of sensitive payment information, reducing the risk of errors or fraud. The method may also include additional steps such as encrypting the converted text before transmission to enhance security. This approach streamlines payment processing by automating the conversion of unstructured data into a format compatible with existing payment networks, improving efficiency and reducing manual intervention.
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January 5, 2021
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